Multi-modal Health Information Technology Innovations for Precision Management of Glaucoma

项目摘要/摘要 青光眼是世界上导致不可逆转失明的主要原因。早期发现和治疗 是危急的，因为症状通常在疾病进展之前不会出现。数据驱动的 需要精确的医学方法来更好地识别那些面临最大风险的人 罹患这种疾病的人，进展迅速导致视力丧失的风险最大。而当 在改善青光眼的眼睛成像和测试方面已经取得了相当大的进展 如果不考虑患者的病情，青光眼的监测、精确管理是不完整的 共存的全身性疾病，同时进行全身药物和治疗，以及 坚持青光眼处方治疗。这些因素对于提供 更全面地看待青光眼管理和改善患者 结果，但对它们的研究相对较少。 在家长奖中，我正在应用健康信息技术的多模式进步 (IT)解决这些差距并实现以下具体目标：(1)开发机器 基于学习的预测模型对青光眼进展风险患者进行分类 来自全国不同患者队列(NIH)的系统电子健康记录(EHR)数据 我们所有人的研究计划)；(2)评估如何整合诺华公司的血压(BP)数据 基于智能手表的家庭BP监测仪增强了风险分层的预测模型 青光眼，以及(3)使用创新的柔性电子设备测量青光眼用药依从性 传感器以验证其在未来干预中的使用，旨在提高依从性和临床 青光眼的转归。 在这项行政补充建议中，我打算在我现有研究的基础上，通过 继续分析美国国立卫生研究院所有研究人员工作台的数据。我会利用我的 与我们所有人都有广泛的经验，并将进行研究，以更好地了解 青光眼与健康的社会决定因素、物质等因素的关系 使用、可穿戴/活动数据和遗传学。这将扩大我现有研究的影响 该计划旨在改善风险分层并产生新的治疗目标 青光眼患者。

PROJECT SUMMARY/ABSTRACT Glaucoma is the world's leading cause of irreversible blindness. Early detection and treatment are critical, as symptoms typically do not present until the disease is advanced. A data-driven precision medicine approach is needed to better identify individuals who are at greatest risk of developing the disease and who are at greatest risk of progressing quickly to vision loss. While there has been considerable progress in eye imaging and testing to improve glaucoma monitoring, precision management of glaucoma is incomplete without accounting for patients' co-existing systemic conditions, concurrent systemic medications and treatments, and adherence with prescribed glaucoma treatment. These factors are important for providing a more comprehensive perspective of glaucoma management and for improving patient outcomes, yet they are relatively understudied. In the parent award, I am applying multi-modal advancements in health information technology (IT) to address these gaps and achieve the following specific aims: (1) Develop machine learning-based predictive models classifying patients at risk for glaucoma progression using systemic electronic health record (EHR) data from a diverse nationwide patient cohort (the NIH All of Us Research Program); (2) evaluate how integrating blood pressure (BP) data from novel smartwatch-based home BP monitors enhance predictive models for risk stratification in glaucoma, and (3) measure glaucoma medication adherence using innovative flexible electronic sensors to validate their use for future interventions aimed at improving adherence and clinical outcomes in glaucoma. In this proposal for an administrative supplement, I intend to build upon my existing studies by continuing to analyze data from the NIH All of Us Researcher Workbench. I will leverage my extensive experience with All of Us and will conduct research to better understand the relationships between glaucoma and factors such as social determinants of health, substance use, wearable/activity data, and genetics. This will expand the impact of my existing research program, which aims to improve risk stratification and generate novel therapeutic targets for glaucoma patients.